Industry Expert Blogs

Research into neural network architectures generally prioritises accuracy over efficiency. Certain papers have investigated efficiency (Tan and Le 2020) (Sandler, et al. 2018), but quite often this is with CPU- or GPU-based rather than accelerator-based inference in mind.

In this original work from Imagination’s AI Research team, many well-known classification networks trained on ImageNet are evaluated. We are not interested in accuracy or cost in their own right, but rather in efficiency, which is a combination of the two. In other words, we want networks that get high accuracy on our IMG Series4 NNAs at as low a cost as possible. We cover:

• identifying ImageNet classification network architectures that give the best accuracy/performance trade-offs on our Series4 NNAs.
• reducing cost dramatically using quantisation-aware training (QAT) and low-precision weights without affecting accuracy.

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